

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	(UnCLIPScheduler,)




							def       snake_case			(					self    ,						**__UpperCAmelCase						)		->       List[str]:
														A      :					Optional[Any]      		=					{
														    '''num_train_timesteps''': 10_00,
														    '''variance_type''': '''fixed_small_log''',
														    '''clip_sample''': True,
														    '''clip_sample_range''': 1.0,
														    '''prediction_type''': '''epsilon''',
														}

														config.update(**__UpperCAmelCase						)
														return config




							def       snake_case			(					self						)		->       Dict:
														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														for variance in ["fixed_small_log", "learned_range"]:
																					self.check_over_configs(variance_type=__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Dict:
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														for clip_sample_range in [1, 5, 10, 20]:
																					self.check_over_configs(clip_sample_range=__UpperCAmelCase						)




							def       snake_case			(					self						)		->       List[str]:
														for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(prediction_type=__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														for time_step in [0, 5_00, 9_99]:
																					for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																												if prev_timestep is not None and prev_timestep >= time_step:
																																			continue

																												self.check_over_forward(time_step=__UpperCAmelCase    ,						prev_timestep=__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Any      		=					self.scheduler_classes[0]
														A      :					Dict      		=					self.get_scheduler_config(variance_type='''fixed_small_log'''						)
														A      :					int      		=					scheduler_class(**__UpperCAmelCase						)

														assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0_000E-10						)						) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_5_4_9_6_2_5						)						) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_9_9_4_9_8_7						)						) < 1E-5




							def       snake_case			(					self						)		->       List[str]:
														A      :					Optional[int]      		=					self.scheduler_classes[0]
														A      :					List[str]      		=					self.get_scheduler_config(variance_type='''learned_range'''						)
														A      :					Optional[Any]      		=					scheduler_class(**__UpperCAmelCase						)

														A      :					Dict      		=					0.5

														assert scheduler._get_variance(1    ,						predicted_variance=__UpperCAmelCase						) - -1_0.1_7_1_2_7_9_0 < 1E-5
														assert scheduler._get_variance(4_87    ,						predicted_variance=__UpperCAmelCase						) - -5.7_9_9_8_0_5_2 < 1E-5
														assert scheduler._get_variance(9_99    ,						predicted_variance=__UpperCAmelCase						) - -0.0_0_1_0_0_1_1 < 1E-5




							def       snake_case			(					self						)		->       int:
														A      :					Union[str, Any]      		=					self.scheduler_classes[0]
														A      :					str      		=					self.get_scheduler_config()
														A      :					List[Any]      		=					scheduler_class(**__UpperCAmelCase						)

														A      :					int      		=					scheduler.timesteps

														A      :					Tuple      		=					self.dummy_model()
														A      :					Union[str, Any]      		=					self.dummy_sample_deter
														A      :					str      		=					torch.manual_seed(0						)

														for i, t in enumerate(__UpperCAmelCase						):
																					# 1. predict noise residual
																					A      :					Optional[int]      		=					model(__UpperCAmelCase    ,						__UpperCAmelCase						)

																					# 2. predict previous mean of sample x_t-1
																					A      :					Any      		=					scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						generator=__UpperCAmelCase						).prev_sample

																					A      :					List[str]      		=					pred_prev_sample

														A      :					Optional[Any]      		=					torch.sum(torch.abs(__UpperCAmelCase						)						)
														A      :					Optional[int]      		=					torch.mean(torch.abs(__UpperCAmelCase						)						)

														assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5						) < 1E-2
														assert abs(result_mean.item() - 0.3_2_8_4_7_4_3						) < 1E-3




							def       snake_case			(					self						)		->       Any:
														A      :					List[Any]      		=					self.scheduler_classes[0]
														A      :					Any      		=					self.get_scheduler_config()
														A      :					Optional[Any]      		=					scheduler_class(**__UpperCAmelCase						)

														scheduler.set_timesteps(25						)

														A      :					int      		=					scheduler.timesteps

														A      :					Union[str, Any]      		=					self.dummy_model()
														A      :					Tuple      		=					self.dummy_sample_deter
														A      :					Tuple      		=					torch.manual_seed(0						)

														for i, t in enumerate(__UpperCAmelCase						):
																					# 1. predict noise residual
																					A      :					Any      		=					model(__UpperCAmelCase    ,						__UpperCAmelCase						)

																					if i + 1 == timesteps.shape[0]:
																												A      :					List[Any]      		=					None
																					else:
																												A      :					List[str]      		=					timesteps[i + 1]

																					# 2. predict previous mean of sample x_t-1
																					A      :					Optional[Any]      		=					scheduler.step(
																					    __UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						prev_timestep=__UpperCAmelCase    ,						generator=__UpperCAmelCase						).prev_sample

																					A      :					List[str]      		=					pred_prev_sample

														A      :					Dict      		=					torch.sum(torch.abs(__UpperCAmelCase						)						)
														A      :					Union[str, Any]      		=					torch.mean(torch.abs(__UpperCAmelCase						)						)

														assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3						) < 1E-2
														assert abs(result_mean.item() - 0.3_3_6_2_0_3_8						) < 1E-3




							def       snake_case			(					self						)		->       Optional[Any]:
														pass






							def       snake_case			(					self						)		->       Any:
														pass









import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






